{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-05-21T13:29:34.432868900Z",
     "start_time": "2024-05-21T13:29:30.923527400Z"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 设置中文字体和负号正常显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 读取数据  \n",
    "df = pd.read_csv('./Lingcang202001-202312.csv')\n",
    "df = df[['year', 'month', 'day', 'average']]  # 筛选需要的列  \n",
    "\n",
    "# 将年月日合并为一个日期列，方便筛选  \n",
    "df['date'] = pd.to_datetime(df[['year', 'month', 'day']])\n",
    "\n",
    "# 训练集: 2020年1月1日-2022年11月30日  \n",
    "train_start = datetime(2020, 1, 1)\n",
    "train_end = datetime(2022, 11, 30)\n",
    "df_train = df[(df['date'] >= train_start) & (df['date'] <= train_end)]\n",
    "\n",
    "# 验证集: 2022年12月1日-2022年12月31日  \n",
    "val_start = datetime(2022, 12, 1)\n",
    "val_end = datetime(2022, 12, 31)\n",
    "df_val = df[(df['date'] >= val_start) & (df['date'] <= val_end)]\n",
    "\n",
    "# 测试集: 2023年1月1日-2023年12月31日  \n",
    "test_start = datetime(2023, 1, 1)\n",
    "test_end = datetime(2023, 12, 31)\n",
    "df_test = df[(df['date'] >= test_start) & (df['date'] <= test_end)]\n",
    "\n",
    "# 删除原始的日期列，如果不需要的话  \n",
    "df_train = df_train.drop('date', axis=1)\n",
    "df_val = df_val.drop('date', axis=1)\n",
    "df_test = df_test.drop('date', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9507c2a5a8ff4a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:29:53.358036900Z",
     "start_time": "2024-05-21T13:29:34.433867200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "67/67 [==============================] - 1s 4ms/step - loss: 325.0382 - val_loss: 72.7081\n",
      "Epoch 2/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 161.8060 - val_loss: 44.8499\n",
      "Epoch 3/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 36.5986 - val_loss: 187.5555\n",
      "Epoch 4/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 29.2769 - val_loss: 163.9878\n",
      "Epoch 5/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 26.2815 - val_loss: 157.1801\n",
      "Epoch 6/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 23.7092 - val_loss: 135.1887\n",
      "Epoch 7/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 21.6320 - val_loss: 140.2951\n",
      "Epoch 8/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 19.7353 - val_loss: 115.1233\n",
      "Epoch 9/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 18.1252 - val_loss: 113.5407\n",
      "Epoch 10/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 16.7610 - val_loss: 99.7330\n",
      "Epoch 11/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 15.6575 - val_loss: 87.5491\n",
      "Epoch 12/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 14.8371 - val_loss: 90.4116\n",
      "Epoch 13/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 14.1816 - val_loss: 78.6274\n",
      "Epoch 14/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 13.6889 - val_loss: 64.3864\n",
      "Epoch 15/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 13.0619 - val_loss: 59.9848\n",
      "Epoch 16/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 12.5825 - val_loss: 62.0942\n",
      "Epoch 17/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 12.0156 - val_loss: 58.9404\n",
      "Epoch 18/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 11.3904 - val_loss: 48.2241\n",
      "Epoch 19/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 10.8001 - val_loss: 52.8139\n",
      "Epoch 20/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 10.2181 - val_loss: 58.6174\n",
      "Epoch 21/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 9.5135 - val_loss: 51.2996\n",
      "Epoch 22/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 8.7662 - val_loss: 42.0576\n",
      "Epoch 23/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 7.9651 - val_loss: 40.9638\n",
      "Epoch 24/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 7.0901 - val_loss: 29.0694\n",
      "Epoch 25/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 6.3302 - val_loss: 36.4084\n",
      "Epoch 26/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 5.7022 - val_loss: 32.5194\n",
      "Epoch 27/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 5.1998 - val_loss: 25.3779\n",
      "Epoch 28/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 4.7985 - val_loss: 26.7416\n",
      "Epoch 29/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 4.4136 - val_loss: 17.2352\n",
      "Epoch 30/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 4.1368 - val_loss: 16.4426\n",
      "Epoch 31/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 3.9761 - val_loss: 16.5315\n",
      "Epoch 32/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 3.7212 - val_loss: 15.0720\n",
      "Epoch 33/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 3.5976 - val_loss: 15.5941\n",
      "Epoch 34/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 3.4715 - val_loss: 10.2969\n",
      "Epoch 35/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 3.4008 - val_loss: 11.1132\n",
      "Epoch 36/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 3.4232 - val_loss: 8.9252\n",
      "Epoch 37/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 3.2480 - val_loss: 10.5991\n",
      "Epoch 38/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 3.1668 - val_loss: 8.2682\n",
      "Epoch 39/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 3.1620 - val_loss: 9.7606\n",
      "Epoch 40/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 3.0914 - val_loss: 8.3602\n",
      "Epoch 41/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 3.0381 - val_loss: 7.5451\n",
      "Epoch 42/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.9887 - val_loss: 7.5633\n",
      "Epoch 43/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.9498 - val_loss: 6.8430\n",
      "Epoch 44/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.9025 - val_loss: 6.6512\n",
      "Epoch 45/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.8833 - val_loss: 5.8750\n",
      "Epoch 46/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.8684 - val_loss: 6.2157\n",
      "Epoch 47/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.7959 - val_loss: 4.3337\n",
      "Epoch 48/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 2.8184 - val_loss: 5.2586\n",
      "Epoch 49/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.8007 - val_loss: 5.0217\n",
      "Epoch 50/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.7856 - val_loss: 5.6808\n",
      "Epoch 51/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.7179 - val_loss: 5.7105\n",
      "Epoch 52/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.7767 - val_loss: 5.9376\n",
      "Epoch 53/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.7310 - val_loss: 5.2363\n",
      "Epoch 54/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 2.6691 - val_loss: 4.5763\n",
      "Epoch 55/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.6474 - val_loss: 6.2508\n",
      "Epoch 56/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.6821 - val_loss: 4.1992\n",
      "Epoch 57/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.6336 - val_loss: 4.7571\n",
      "Epoch 58/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.6471 - val_loss: 4.2102\n",
      "Epoch 59/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.6103 - val_loss: 5.1262\n",
      "Epoch 60/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.6004 - val_loss: 3.5184\n",
      "Epoch 61/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.6022 - val_loss: 4.4289\n",
      "Epoch 62/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5878 - val_loss: 4.1572\n",
      "Epoch 63/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5623 - val_loss: 4.3603\n",
      "Epoch 64/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5598 - val_loss: 3.5225\n",
      "Epoch 65/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.6412 - val_loss: 3.4380\n",
      "Epoch 66/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5717 - val_loss: 3.9344\n",
      "Epoch 67/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5328 - val_loss: 3.1672\n",
      "Epoch 68/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5497 - val_loss: 3.5580\n",
      "Epoch 69/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5376 - val_loss: 3.0223\n",
      "Epoch 70/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4863 - val_loss: 4.6997\n",
      "Epoch 71/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5042 - val_loss: 3.1515\n",
      "Epoch 72/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5383 - val_loss: 3.5379\n",
      "Epoch 73/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5600 - val_loss: 4.2494\n",
      "Epoch 74/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5684 - val_loss: 4.1851\n",
      "Epoch 75/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5767 - val_loss: 3.1812\n",
      "Epoch 76/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4641 - val_loss: 2.9782\n",
      "Epoch 77/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4846 - val_loss: 2.9266\n",
      "Epoch 78/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4717 - val_loss: 3.5011\n",
      "Epoch 79/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4701 - val_loss: 3.7974\n",
      "Epoch 80/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4771 - val_loss: 2.8421\n",
      "Epoch 81/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4972 - val_loss: 3.1837\n",
      "Epoch 82/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5232 - val_loss: 3.3082\n",
      "Epoch 83/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 2.4619 - val_loss: 3.6360\n",
      "Epoch 84/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4463 - val_loss: 3.8163\n",
      "Epoch 85/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4682 - val_loss: 2.9979\n",
      "Epoch 86/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4404 - val_loss: 3.1290\n",
      "Epoch 87/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4438 - val_loss: 2.9146\n",
      "Epoch 88/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4376 - val_loss: 3.0336\n",
      "Epoch 89/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4523 - val_loss: 4.0790\n",
      "Epoch 90/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.5060 - val_loss: 2.9066\n",
      "Epoch 91/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4573 - val_loss: 2.8318\n",
      "Epoch 92/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4015 - val_loss: 2.8260\n",
      "Epoch 93/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4085 - val_loss: 2.8471\n",
      "Epoch 94/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4753 - val_loss: 3.4667\n",
      "Epoch 95/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4804 - val_loss: 2.7655\n",
      "Epoch 96/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4388 - val_loss: 3.0382\n",
      "Epoch 97/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.4251 - val_loss: 3.5055\n",
      "Epoch 98/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.3838 - val_loss: 3.3503\n",
      "Epoch 99/100\n",
      "67/67 [==============================] - 0s 3ms/step - loss: 2.3824 - val_loss: 2.8757\n",
      "Epoch 100/100\n",
      "67/67 [==============================] - 0s 2ms/step - loss: 2.3955 - val_loss: 3.0795\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据归一化 \n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# 拟合训练集数据 \n",
    "X_train_scaled = scaler.fit_transform(df_train[['year', 'month', 'day']])\n",
    "y_train = df_train['average'].values\n",
    "\n",
    "# 使用相同的scaler转换验证集和测试集数据  \n",
    "X_val_scaled = scaler.transform(df_val[['year', 'month', 'day']])\n",
    "y_val = df_val['average'].values\n",
    "\n",
    "X_test_scaled = scaler.transform(df_test[['year', 'month', 'day']])\n",
    "y_test = df_test['average'].values\n",
    "\n",
    "# 创建一个简单的MLP模型  \n",
    "model = Sequential()  \n",
    "model.add(Dense(64, activation='relu', input_shape=(3,)))  # 输入层输入维度为3\n",
    "model.add(Dense(32, activation='relu'))  # 隐藏层\n",
    "model.add(Dense(1))  # 用于预测平均温度的输出层 \n",
    "\n",
    "# 编译模型\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# 训练模型并获取训练历史  \n",
    "history = model.fit(X_train_scaled, y_train, epochs=100, batch_size=16, validation_data=(X_val_scaled, y_val))\n",
    "\n",
    "# 绘制训练损失和验证损失  \n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(history.history['loss'], label='训练损失')\n",
    "plt.plot(history.history['val_loss'], label='验证损失')\n",
    "plt.title('训练与验证损失')\n",
    "plt.xlabel('训练轮次')\n",
    "plt.ylabel('损失')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3213c488c0e2c4a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:29:53.683496400Z",
     "start_time": "2024-05-21T13:29:53.364022Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 33ms/step - loss: 3.0795\n",
      "Validation Loss: 3.079538106918335\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      " Mean Absolute Error (MAE): 1.23\n",
      "Mean Squared Error (MSE): 3.08\n",
      "Root Mean Squared Error (RMSE): 1.75\n",
      "Mean Absolute Percentage Error (MAPE): 11.93%\n",
      "R2 Score (R2): 0.27\n",
      "Scaler saved to ./mlp_scaler.joblib\n",
      "模型已保存为 MPL_temperature_prediction_model.h5\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# 评估模型  \n",
    "val_loss = model.evaluate(X_val_scaled, y_val)\n",
    "print(f\"Validation Loss: {val_loss}\")\n",
    "\n",
    "# 计算验证集的平均绝对误差\n",
    "predictions = model.predict(X_val_scaled)\n",
    "mae = np.mean(np.abs(predictions.flatten() - y_val))\n",
    "print(f\" Mean Absolute Error (MAE): {mae:.2f}\")\n",
    "\n",
    "# 计算验证集的均方误差\n",
    "mse = np.mean((predictions.flatten() - y_val) ** 2)\n",
    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
    "\n",
    "# 计算验证集的均方根误差\n",
    "rmse = np.sqrt(mse)\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
    "\n",
    "# 计算验证集的平均绝对百分比误差\n",
    "mape = np.mean(np.abs(predictions.flatten() - y_val) / y_val) * 100\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")\n",
    "\n",
    "# 计算验证集的R2分数\n",
    "r2 = 1 - (np.sum((y_val - predictions.flatten()) ** 2) / np.sum((y_val - np.mean(y_val)) ** 2))\n",
    "print(f\"R2 Score (R2): {r2:.2f}\")\n",
    "\n",
    "# 保存scaler  \n",
    "scaler_path = './mlp_scaler.joblib'  # 定义scaler保存的文件路径  \n",
    "joblib.dump(scaler, scaler_path)  # 使用joblib保存scaler对象  \n",
    "print(f\"Scaler saved to {scaler_path}\")\n",
    "\n",
    "# 保存整个模型  \n",
    "model.save('MPL_temperature_prediction_model.h5')\n",
    "print(\"模型已保存为 MPL_temperature_prediction_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad8217ee2519522b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:29:54.035009100Z",
     "start_time": "2024-05-21T13:29:53.608695700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "# 预测测试集数据 \n",
    "predictions = model.predict(X_test_scaled)\n",
    "\n",
    "# 将测试集的年月日信息转换为Matplotlib可以理解的日期格式  \n",
    "dates = [mdates.date2num(datetime(year, month, day))\n",
    "         for year, month, day in zip(df_test['year'], df_test['month'], df_test['day'])]\n",
    "\n",
    "# 绘制预测值和真实值的对比图  \n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# 绘制实际温度和预测温度的折线图  \n",
    "plt.plot(mdates.num2date(dates), y_test, label='Actual Temperature', color='blue')\n",
    "plt.plot(mdates.num2date(dates), predictions.flatten(), label='Predicted Temperature', color='red', linestyle='--')\n",
    "\n",
    "# 设置图表标题和坐标轴标签  \n",
    "plt.title('Actual vs Predicted Average Temperature in 2023')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Average Temperature')\n",
    "\n",
    "# 设置x轴日期格式  \n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.MonthLocator())\n",
    "plt.gcf().autofmt_xdate()\n",
    "\n",
    "# 添加图例  \n",
    "plt.legend()\n",
    "\n",
    "# 显示图表  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2648484cf2ac65e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:29:54.074951300Z",
     "start_time": "2024-05-21T13:29:54.028030Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 1.61\n",
      "Mean Squared Error: 3.85\n",
      "Root Mean Squared Error: 1.96\n",
      "Mean Absolute Percentage Error: 8.92%\n",
      "R2 Score: 0.74\n"
     ]
    }
   ],
   "source": [
    "# 计算测试集的平均绝对误差\n",
    "mae = np.mean(np.abs(predictions.flatten() - y_test))\n",
    "print(f\"Mean Absolute Error: {mae:.2f}\")\n",
    "\n",
    "# 计算测试集的均方误差\n",
    "mse = np.mean((predictions.flatten() - y_test) ** 2)\n",
    "print(f\"Mean Squared Error: {mse:.2f}\")\n",
    "\n",
    "# 计算测试集的均方根误差\n",
    "rmse = np.sqrt(mse)\n",
    "print(f\"Root Mean Squared Error: {rmse:.2f}\")\n",
    "\n",
    "# 计算测试集的平均绝对百分比误差\n",
    "mape = np.mean(np.abs(predictions.flatten() - y_test) / y_test) * 100\n",
    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
    "\n",
    "# 计算测试集的R2分数\n",
    "r2 = 1 - (np.sum((y_test - predictions.flatten()) ** 2) / np.sum((y_test - np.mean(y_test)) ** 2))\n",
    "print(f\"R2 Score: {r2:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d12a75f1fb332919",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:29:54.183733400Z",
     "start_time": "2024-05-21T13:29:54.042989700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n",
      "The predicted average temperature for January 1, 2024 is: 11.18°C\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "The predicted average temperature for May 20, 2024 is: 19.52°C\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming you have a pre-trained model and a scaler fitted on a DataFrame with columns ['year', 'month', 'day']  \n",
    "\n",
    "def predict_temperature(year, month, day):\n",
    "    # 创建一个 DataFrame，其列名与用于适应缩放器的列名相同 \n",
    "    input_data = pd.DataFrame({'year': [year], 'month': [month], 'day': [day]})\n",
    "\n",
    "    # 对输入数据进行缩放\n",
    "    input_data_scaled = scaler.transform(input_data)\n",
    "\n",
    "    # 使用模型预测输入数据 \n",
    "    prediction = model.predict(input_data_scaled)\n",
    "\n",
    "    # 返回预测值  \n",
    "    return prediction.flatten()[0]\n",
    "\n",
    "  \n",
    "temperature = predict_temperature(2023, 1, 1)\n",
    "print(f\"The predicted average temperature for January 1, 2024 is: {temperature:.2f}°C\")\n",
    "temperature2 = predict_temperature(2023, 5, 20)\n",
    "print(f\"The predicted average temperature for May 20, 2024 is: {temperature2:.2f}°C\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b67dd06322cbbd8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:29:54.310653300Z",
     "start_time": "2024-05-21T13:29:54.171692700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 17ms/step\n",
      "The predicted average temperature for October 27, 2024 is: 18.38°C\n"
     ]
    }
   ],
   "source": [
    "# 加载模型\n",
    "from keras.models import load_model\n",
    "loaded_model = load_model('MPL_temperature_prediction_model.h5')\n",
    "\n",
    "# 进行预测\n",
    "temperature = predict_temperature(2023, 10, 27)\n",
    "print(f\"The predicted average temperature for October 27, 2024 is: {temperature:.2f}°C\")\n",
    "\n"
   ]
  }
 ],
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